Decision support systems such as troubleshooting and diagnosis systems have become integral tools for corporations, researchers, and individuals. Many corporations have established help desks to respond to user problems with software, hardware, or individual products. Other types of help desk and diagnosis systems such as medical diagnosis, financial planning, and the like have also been established to facilitate customer decision making. Customers expect help desk personnel or diagnosis systems to resolve their problems or provide solutions rapidly and efficiently. Unnecessary or repetitive questions increase customer frustration and negatively impact customer satisfaction.
Many computer-based systems have been developed to assist help-desk personnel, users, or employees to identify solutions based on their observations of attributes associated with the problem. In general, decision support systems such as troubleshooting systems and diagnosis systems include a set of questions, potential (differential) diagnoses, and suggestions for how to proceed in order to remedy the diagnosed problem. Typical methodologies employed in these systems include decision trees and Bayesian networks. The primary difference between systems based upon a decision tree approach and those based upon Bayesian networks is in the method used for selection and ordering of questions to be presented to the user.
Decision trees typically use a static hierarchical ordering of questions. This static ordering makes decision trees simple to explain and implement. However, decision trees often become difficult to maintain and update because of the extensive duplication of questions needed for completeness.
Bayesian networks attempt to minimize the number of questions asked during a differential diagnosis by using probabilistic relationships between the different questions. The probabilistic relationships between problem symptoms identified by the user aid in minimizing the solution path. However, Bayesian network systems are typically difficult to implement because the physical characteristics of the system are not probabilistic and they create entirely different solution paths. The maintenance is also problematic because of the amount of data that is necessary to establish the real world probabilities for any new knowledge that must be added to the Bayesian network.
In conventional decision support systems, the onus is placed on the user to understand the problem language and the solution structure implemented by the system. For example, in most systems, problem areas are identified individually; and therefore, the user has to recognize which problem area is appropriate for the problem. In addition, a user must re-interpret the problem using the syntax of the system. Because of these constraints, the intuitive nature of conventional decision support systems is poor, making them impractical for use by end-user customers.
Furthermore, conventional systems force the user to take a particular path to a solution. For example, a user may have to go through multiple steps to arrive at a solution. These multiple steps may involve answering questions which are irrelevant to the problem being addressed.
Another difficulty faced by conventional decision support systems relates to adding new information into the system. In many conventional systems, updates to system information are handled off-line. For example, when new information or a new solution is identified by a system employee or a knowledge worker, that information is sent to an administrator for entry into the system. Thus, the availability of new information in these systems is dependent upon the efficiency of the administrator.
Because Bayesian networks are monolithic, adding new information to the network is challenging. New information must be fully integrated with all the information already in the network. Thus, the new information may not be available to solve user problems for an extended period of time.
What is needed is a decision support system which rapidly converges on a solution and allows a user to take multiple paths to a solution.
What is further needed is a decision support system in which new information can be incrementally added in real-time and immediately integrated into the system.